The squamous cell carcinoma of the lung (SCLC) is one of the most common types of lung cancer. As GLOBOCAN reported in 2018, lung cancer was the first cause of death and new cases by cancer worldwide. Typically, diagnosis is made in the later stages of the disease with few treatment options available. The goal of this work was to find some key components underlying each stage of the disease, to help in the classification of tumor samples, and to increase the available options for experimental assays and molecular targets that could be used in treatment development. We employed two approaches. The first was based in the classic method of differential gene expression analysis, network analysis, and a novel concept known as network gatekeepers. The second approach was using machine learning algorithms. From our combined approach, we identified two sets of genes that could function as a signature to identify each stage of the cancer pathology. We also arrived at a network of 55 nodes, which according to their biological functions, they can be regarded as drivers in this cancer. Although biological experiments are necessary for their validation, we proposed that all these genes could be used for cancer development treatments.